RACHSRisk Adjustment in Congenital Heart Surgery (evaluation method; cardiology)
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Both of the schemes focus on reducing the impact of RACH congestion on HTC devices.
In [12], RACH resources are preallocated to different MTC classes using class-dependent backoff procedures to prevent a large number of simultaneous RACH access attempts.
In this paper, we model a QL algorithm to control the barring factor in order to minimize both the number of RACH collisions and the access delay.
The basic RACH capacity parameters for LTE FDD networks followed [1].
When the granularity is properly coarse (e.g., [delta] = 0.2), the barring factor swiftly copes with the variance of the number of meters trying to access RACH. However, when the granularity is too fine (e.g., [delta] = 0.1), the barring factor does not promptly respond to the variance of RA requests.
In the ACB, because most meters have restricted access to RACH and the competition for channel resources is reduced, the success and collision rates decrease, but both the failure rate and the access delay increase.
The goals of our model were minimizing both the RACH collision rate and the access delay.